GPU Programming with C++ and CUDA

GPU Programming with C++ and CUDA

位教师：Packt - Course Instructors

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10个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

10个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Accelerate real-world tasks using GPU parallelism
Optimize performance with CUDA streams and custom C++ solutions
Create and share GPU libraries with Python integration

您将获得的技能

您将学习的工具

要了解的详细信息

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作业

10 项作业

授课语言：英语（English）

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该课程共有10个模块

In this course, you’ll master GPU programming using C++ and CUDA to significantly enhance your software's performance. By focusing on parallelism, you’ll learn to leverage the full power of GPUs for high-performance computing applications.

You will acquire practical knowledge on managing GPU devices, optimizing GPU resource usage, and integrating GPU code with Python to build scalable and efficient applications. This course emphasizes real-world strategies for optimizing performance and building reusable libraries. This course combines fundamental theory with hands-on applications to help you solve complex performance challenges. You'll not only understand the core concepts but also implement them in real-world projects, such as creating libraries for Python integration. Ideal for C++ developers with experience in basic programming concepts, this course will take you through advanced topics, from parallel algorithms to multi-GPU usage. A background in operating systems is recommended for tackling more complex concepts. Based on the book, GPU Programming with C++ and CUDA, by Paulo Motta.

In this section, we explore parallelism in software, its importance, and the differences between CPU and GPU architectures to build a foundation for GPU programming.

涵盖的内容

2个视频4篇阅读材料1个作业

In this section, we configure a GPU environment using Docker, locate official Linux documentation, and install the CUDA toolkit on Ubuntu 20.04 or 22.04 for AI and machine learning workflows.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we introduce GPU programming fundamentals, including kernel execution, device inspection, and setting up a working environment for CUDA development.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore SIMD execution, data movement, and parallel vector addition for GPU programming.

涵盖的内容

1个视频5篇阅读材料1个作业

In this section, we explore GPU thread, block, and grid configurations, asynchronous data transfer, streams, events, and shared memory to optimize performance in parallel computing.

涵盖的内容

1个视频5篇阅读材料1个作业

In this section, we explore parallel algorithm design, focusing on matrix operations, reduction, and workload balancing for efficient GPU execution.

涵盖的内容

1个视频8篇阅读材料1个作业

1个视频总计1分钟

Parallel Algorithms with CUDA - Overview Video1分钟

8篇阅读材料总计80分钟

Introduction10分钟
Understanding How to Spot and Exploit Parallelism10分钟
Balancing the Workloads10分钟
Computing Matrix Addition and Multiplication10分钟
Calculating Numerical Integrals10分钟
Reducing from Many10分钟
Sorting Data10分钟
Processing Sensor Data with a Convolution10分钟

1个作业总计10分钟

Data Management and Parallel Execution10分钟

In this section, we explore GPU optimization and profile with NVIDIA Nsight Compute.

涵盖的内容

1个视频5篇阅读材料1个作业

1个视频总计1分钟

Performance Strategies - Overview Video1分钟

5篇阅读材料总计100分钟

Introduction10分钟
Profiling with NVIDIA Nsight Compute20分钟
Optimizing to Speed Up Our Code20分钟
Using the Release Configuration20分钟
Using Loop Unroll for Further Improvements30分钟

1个作业总计10分钟

Performance Strategies in Software Development10分钟

In this section, we explore debugging CUDA code with VS Code, using CUDA streams to overlap memory and kernel operations, and configuring multiple GPUs for parallel processing.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore methods to integrate C++ GPU code with Python, focusing on Ctypes, custom wrappers, and performance analysis for efficient cross-language execution.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore GPU development using cuBLAS and Thrust, optimize code for memory and thread efficiency, and test with GTest and Pytest to ensure reliability and performance.